Many companies spend millions of dollars each year on advertising and other marketing activities to improve sales. However, it is very difficult to determine how their marketing activities are impacting their sales. This is primarily due to the many factors that can actually influence sales, which may or may not be related to the marketing activities performed by the companies. For example, economic trends and competitor pricing may impact sales, as well as advertising in relevant marketing channels. As a result, companies have great difficulty focusing their marketing efforts and resources on the activities that are most likely to improve sales.
One approach to determining how a marketing activity impacts sales is to use modeling. Modeling may be used to forecast or predict behavior or outcomes. Models may be generated through a regression analysis or other method of analyzing historic data. For example, companies may use historic sales data to generate a model to predict how sales will be impacted in the future, and these companies may make adjustments to improve sales based on the predictions. However, as indicated above, there are many variables that may be included in the model based on all the factors that may influence sales. Furthermore, some variables may be more accurate than other variables based on insufficient data, inaccuracies and other factors. It is very difficult to select the variables to use in the model that would yield the most accurate forecasting results. Accordingly, many models that may be currently used for forecasting can be inaccurate. Furthermore, it is very difficult to manage the data, especially for large number of variables, so the data can be used to build models. Accordingly, the processing of the data sets to build models may involve immense processing time.